Network analysis of psoriasis reveals biological pathways and roles for coding and long non-coding RNAs.
Richard Ahn, Rashmi Gupta, Kevin Lai, Nitin Chopra, Sarah T Arron, Wilson Liao
Author Information
- Richard Ahn: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA. richard.ahn@ucsf.edu. ORCID
- Rashmi Gupta: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA.
- Kevin Lai: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA.
- Nitin Chopra: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA.
- Sarah T Arron: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA.
- Wilson Liao: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA.
BACKGROUND: Psoriasis is an immune-mediated, inflammatory disorder of the skin characterized by chronic inflammation and hyperproliferation of the epidermis. Differential expression analysis of microarray or RNA-seq data have shown that thousands of coding and non-coding genes are differentially expressed between psoriatic and healthy control skin. However, differential expression analysis may fail to detect perturbations in gene coexpression networks. Sensitive detection of such networks may provide additional insight into important disease-associated pathways. In this study, we applied weighted gene coexpression network analysis (WGCNA) on RNA-seq data from psoriasis patients and healthy controls.
RESULTS: RNA-seq was performed on skin samples from 18 psoriasis patients (pre-treatment and post-treatment with the TNF-α inhibitor adalimumab) and 16 healthy controls, generating an average of 52.3 million 100-bp paired-end reads per sample. Using WGCNA, we identified 3 network modules that were significantly correlated with psoriasis and 6 network modules significantly correlated with biologic treatment, with only 16 % of the psoriasis-associated and 5 % of the treatment-associated coexpressed genes being identified by differential expression analysis. In a majority of these correlated modules, more than 50 % of coexpressed genes were long non-coding RNAs (lncRNA). Enrichment analysis of these correlated modules revealed that short-chain fatty acid metabolism and olfactory signaling are amongst the top pathways enriched for in modules associated with psoriasis, while regulation of leukocyte mediated cytotoxicity and regulation of cell killing are amongst the top pathways enriched for in modules associated with biologic treatment. A putative autoantigen, LL37, was coexpressed in the module most correlated with psoriasis.
CONCLUSIONS: This study has identified several networks of coding and non-coding genes associated with psoriasis and biologic drug treatment, including networks enriched for short-chain fatty acid metabolism and olfactory receptor activity, pathways that were not previously identified through differential expression analysis and may be dysregulated in psoriatic skin. As these networks are comprised mostly of non-coding genes, it is likely that non-coding genes play critical roles in the regulation of pathways involved in the pathogenesis of psoriasis.
Adult
Case-Control Studies
Female
Gene Expression Profiling
Gene Expression Regulation
Gene Regulatory Networks
Humans
Male
Middle Aged
Psoriasis
RNA, Long Noncoding
RNA, Messenger
Signal Transduction
Skin
Transcriptome
RNA, Long Noncoding
RNA, Messenger
